Add SWIFT coding agent probe experiment scripts

.gitignore

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+.venv/
+__pycache__/
+*.py[cod]
+.pytest_cache/
+data/
+models/
+outputs/
+runs/
+logs/
+*.log

.gitmodules

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+[submodule "third_party/modelscope-swift"]
+	path = third_party/modelscope-swift
+	url = https://github.com/modelscope/ms-swift.git

README.md

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+# TI Coding Agent Training Probe
+
+这个仓库用于复现一组 coding-agent SFT probing 实验：从 Hugging Face 下载已经构造好的 Open-SWE-Traces probe 数据集，下载 Qwen3.5-9B 和 Qwen3.6-27B，然后用 ModelScope-SWIFT 依次跑四个 1 epoch 训练任务。
+
+## 目录
+
+- `third_party/modelscope-swift/`: ModelScope-SWIFT submodule。
+- `scripts/setup_env.sh`: 一键创建 repo 内 `.venv` 并安装本项目和 SWIFT。
+- `scripts/download_dataset.py`: 下载 Hugging Face 数据集并解压 `train.jsonl`、`validation.jsonl`。
+- `scripts/download_models.sh`: 下载 Qwen3.5-9B 和 Qwen3.6-27B 到 `models/`。
+- `scripts/train_qwen35_9b_lora.sh`: Qwen3.5-9B rank=32 LoRA。
+- `scripts/train_qwen35_9b_full.sh`: Qwen3.5-9B bf16 full SFT。
+- `scripts/train_qwen36_27b_lora.sh`: Qwen3.6-27B rank=32 LoRA。
+- `scripts/train_qwen36_27b_full.sh`: Qwen3.6-27B bf16 full SFT。
+- `scripts/run_all_experiments.sh`: 按 LoRA 9B -> full 9B -> LoRA 27B -> full 27B 的顺序执行完整实验。
+- `runs/`: TensorBoard 日志目录。
+- `logs/`: 训练 stdout/stderr 和实际命令记录。
+- `outputs/`: checkpoint 和最终模型权重输出目录。
+- `data/`: 下载后的训练和验证数据，默认不进 git。
+- `models/`: 下载后的 base model，默认不进 git。
+
+## 环境部署
+
+在 B300 上使用：
+
+```bash
+cd /ssd/workspace/yi/ti_coding_agent_probe
+git submodule update --init --recursive
+./scripts/setup_env.sh
+```
+
+脚本会显式设置 B300 代理：
+
+```bash
+http://100.72.0.101:8888
+```
+
+Python 依赖安装在仓库内 `.venv`，不会写入系统 Python。
+
+## 下载数据集
+
+数据集默认名是 `ti_coding_agent_training_probe_20260624`。如果环境里设置了 `HF_TOKEN`，脚本会用 token owner 自动拼成 `owner/ti_coding_agent_training_probe_20260624`。也可以显式指定：
+
+```bash
+export HF_ENDPOINT=https://hf-mirror.com
+export HF_DATASET_REPO_ID=<owner>/ti_coding_agent_training_probe_20260624
+./scripts/download_dataset.py
+```
+
+输出：
+
+- `data/raw/training_probe/`: Hugging Face snapshot。
+- `data/processed/training_probe/train.jsonl`
+- `data/processed/training_probe/validation.jsonl`
+
+训练数据里 `system`、`user`、`tool` 消息带 `loss=false`，只有 assistant 轨迹带 `loss=true`。system prompt 会作为上下文参与 attention，但不作为预测目标计算 loss。
+
+## 下载模型
+
+```bash
+./scripts/download_models.sh
+```
+
+默认模型 ID：
+
+- `Qwen/Qwen3.5-9B`
+- `Qwen/Qwen3.6-27B`
+
+如果 Hugging Face 上实际模型 ID 有变化，可以覆盖：
+
+```bash
+export QWEN35_9B_MODEL_ID=<actual-9b-id>
+export QWEN36_27B_MODEL_ID=<actual-27b-id>
+./scripts/download_models.sh
+```
+
+## 单步训练
+
+每个训练脚本默认：
+
+- `num_train_epochs=1`
+- `lora_rank=32`
+- `torch_dtype=bfloat16`
+- `save_steps=1000`
+- `eval_steps=1000`
+- `report_to=tensorboard`
+- `max_length=262144`
+- `warmup_ratio=0.1`
+- `learning_rate=1e-5`
+
+命令：
+
+```bash
+./scripts/train_qwen35_9b_lora.sh
+./scripts/train_qwen35_9b_full.sh
+./scripts/train_qwen36_27b_lora.sh
+./scripts/train_qwen36_27b_full.sh
+```
+
+## 一键完整实验
+
+确认 GPU 空闲后执行：
+
+```bash
+./scripts/run_all_experiments.sh
+```
+
+执行顺序固定为：
+
+1. Qwen3.5-9B LoRA
+2. Qwen3.5-9B bf16 full SFT
+3. Qwen3.6-27B LoRA
+4. Qwen3.6-27B bf16 full SFT
+
+## TensorBoard
+
+```bash
+./scripts/tensorboard.sh
+```
+
+训练日志会写到 `runs/<run_name>/`。SWIFT/Transformers 的 TensorBoard 标量通常包括 loss、learning rate、eval loss、runtime、samples/sec、steps/sec 等 throughput 指标；同时 stdout 会保存在 `logs/<run_name>.log`。

SKILL.md

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+# TI Coding Agent Probe Skill
+
+Use this skill when the user wants to run or modify the TI coding-agent SFT probe experiments based on the Hugging Face dataset `ti_coding_agent_training_probe_20260624`.
+
+## Repository Contract
+
+- Work in `/ssd/workspace/yi/ti_coding_agent_probe` on B300 unless the user says otherwise.
+- Use the B300 proxy for all network access:
+  - `http_proxy=http://100.72.0.101:8888`
+  - `https_proxy=http://100.72.0.101:8888`
+  - `HF_ENDPOINT=https://hf-mirror.com`
+- Do not install Python packages globally. Use the repository-local `.venv` created by `scripts/setup_env.sh`.
+- Do not start GPU training before checking GPU occupancy with `nvidia-smi`.
+- Do not commit or upload `data/`, `models/`, `outputs/`, `runs/`, or `logs/`.
+
+## Key Commands
+
+Initialize the repo and environment:
+
+```bash
+git submodule update --init --recursive
+./scripts/setup_env.sh
+```
+
+Download the training probe dataset:
+
+```bash
+export HF_ENDPOINT=https://hf-mirror.com
+export HF_DATASET_REPO_ID=<owner>/ti_coding_agent_training_probe_20260624
+./scripts/download_dataset.py
+```
+
+Download base models:
+
+```bash
+./scripts/download_models.sh
+```
+
+Run the full ordered experiment:
+
+```bash
+./scripts/run_all_experiments.sh
+```
+
+Run only one stage:
+
+```bash
+./scripts/train_qwen35_9b_lora.sh
+./scripts/train_qwen35_9b_full.sh
+./scripts/train_qwen36_27b_lora.sh
+./scripts/train_qwen36_27b_full.sh
+```
+
+Open TensorBoard:
+
+```bash
+./scripts/tensorboard.sh
+```
+
+## Training Semantics
+
+The dataset uses SWIFT-style chat messages. `system`, `user`, and `tool` messages should remain masked with `loss=false`; only assistant trajectories should contribute to loss. This keeps scaffold prompts and tool outputs as conditioning context rather than targets to memorize.
+
+The default experiment uses:
+
+- 1 epoch
+- LoRA rank 32 for LoRA runs
+- bf16 full fine-tuning for full runs
+- `max_length=262144`
+- checkpoint save every 1000 steps
+- validation every 1000 steps
+- TensorBoard logging under `runs/`
+
+Override model IDs or paths with:
+
+```bash
+export QWEN35_9B_MODEL_ID=<hf-id>
+export QWEN36_27B_MODEL_ID=<hf-id>
+export QWEN35_9B_MODEL_PATH=<local-path>
+export QWEN36_27B_MODEL_PATH=<local-path>
+```

pyproject.toml

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+[build-system]
+requires = ["setuptools>=68", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "ti-coding-agent-probe"
+version = "0.1.0"
+description = "Reproducible SWIFT SFT probe scripts for Open-SWE-Traces derived coding-agent data."
+requires-python = ">=3.10"
+dependencies = [
+  "huggingface_hub>=0.23",
+  "datasets>=2.20",
+  "pyarrow>=15",
+  "tensorboard>=2.15",
+]
+
+[tool.setuptools.packages.find]
+where = ["src"]

scripts/download_dataset.py

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+#!/usr/bin/env python3
+from __future__ import annotations
+
+import argparse
+import gzip
+import os
+import shutil
+from pathlib import Path
+
+from huggingface_hub import HfApi, snapshot_download
+
+
+DEFAULT_REPO_NAME = "ti_coding_agent_training_probe_20260624"
+
+
+def resolve_dataset_id(raw: str, token: str | None, endpoint: str | None) -> str:
+    if "/" in raw:
+        return raw
+    if not token:
+        raise SystemExit(
+            "HF_DATASET_REPO_ID must be owner/name when HF_TOKEN is not set. "
+            f"Got unqualified repo name: {raw}"
+        )
+    owner = HfApi(token=token, endpoint=endpoint).whoami()["name"]
+    return f"{owner}/{raw}"
+
+
+def gunzip_if_needed(src: Path, dst: Path) -> None:
+    if dst.exists() and dst.stat().st_size > 0:
+        return
+    dst.parent.mkdir(parents=True, exist_ok=True)
+    with gzip.open(src, "rb") as fin, dst.open("wb") as fout:
+        shutil.copyfileobj(fin, fout, length=1024 * 1024)
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dataset-id", default=os.environ.get("HF_DATASET_REPO_ID", DEFAULT_REPO_NAME))
+    parser.add_argument("--raw-dir", default="data/raw/training_probe")
+    parser.add_argument("--out-dir", default="data/processed/training_probe")
+    args = parser.parse_args()
+
+    token = os.environ.get("HF_TOKEN")
+    endpoint = os.environ.get("HF_ENDPOINT")
+    dataset_id = resolve_dataset_id(args.dataset_id, token, endpoint)
+
+    raw_dir = Path(args.raw_dir)
+    out_dir = Path(args.out_dir)
+    raw_dir.mkdir(parents=True, exist_ok=True)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    local_path = snapshot_download(
+        repo_id=dataset_id,
+        repo_type="dataset",
+        local_dir=raw_dir,
+        token=token,
+        endpoint=endpoint,
+        allow_patterns=[
+            "README.md",
+            "metadata.json",
+            "train.parquet",
+            "validation.parquet",
+            "train.jsonl.gz",
+            "validation.jsonl.gz",
+        ],
+    )
+    local = Path(local_path)
+    for name in ("train", "validation"):
+        gz = local / f"{name}.jsonl.gz"
+        if gz.exists():
+            gunzip_if_needed(gz, out_dir / f"{name}.jsonl")
+        parquet = local / f"{name}.parquet"
+        if parquet.exists():
+            target = out_dir / f"{name}.parquet"
+            if not target.exists():
+                target.symlink_to(parquet.resolve())
+    print(f"DATASET_ID={dataset_id}")
+    print(f"TRAIN_JSONL={out_dir / 'train.jsonl'}")
+    print(f"VALIDATION_JSONL={out_dir / 'validation.jsonl'}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

scripts/download_models.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+
+ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "${ROOT_DIR}"
+
+export http_proxy="${http_proxy:-http://100.72.0.101:8888}"
+export https_proxy="${https_proxy:-http://100.72.0.101:8888}"
+export HTTP_PROXY="${HTTP_PROXY:-${http_proxy}}"
+export HTTPS_PROXY="${HTTPS_PROXY:-${https_proxy}}"
+export HF_ENDPOINT="${HF_ENDPOINT:-https://hf-mirror.com}"
+
+source .venv/bin/activate
+mkdir -p models
+
+QWEN35_9B_MODEL_ID="${QWEN35_9B_MODEL_ID:-Qwen/Qwen3.5-9B}"
+QWEN36_27B_MODEL_ID="${QWEN36_27B_MODEL_ID:-Qwen/Qwen3.6-27B}"
+
+huggingface-cli download "${QWEN35_9B_MODEL_ID}" --local-dir "models/qwen3.5-9b" --local-dir-use-symlinks False
+huggingface-cli download "${QWEN36_27B_MODEL_ID}" --local-dir "models/qwen3.6-27b" --local-dir-use-symlinks False

scripts/run_all_experiments.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+
+ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "${ROOT_DIR}"
+
+./scripts/download_dataset.py
+./scripts/download_models.sh
+
+./scripts/train_qwen35_9b_lora.sh
+./scripts/train_qwen35_9b_full.sh
+./scripts/train_qwen36_27b_lora.sh
+./scripts/train_qwen36_27b_full.sh
+
+echo "All experiments finished."
+echo "TensorBoard: tensorboard --logdir runs --host 0.0.0.0 --port 6006"

scripts/setup_env.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+
+ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "${ROOT_DIR}"
+
+export http_proxy="${http_proxy:-http://100.72.0.101:8888}"
+export https_proxy="${https_proxy:-http://100.72.0.101:8888}"
+export HTTP_PROXY="${HTTP_PROXY:-${http_proxy}}"
+export HTTPS_PROXY="${HTTPS_PROXY:-${https_proxy}}"
+export PIP_INDEX_URL="${PIP_INDEX_URL:-https://mirrors.aliyun.com/pypi/simple/}"
+
+python3 -m venv .venv
+source .venv/bin/activate
+python -m pip install -U pip setuptools wheel
+python -m pip install -e .
+python -m pip install -e third_party/modelscope-swift
+
+mkdir -p data/raw data/processed models outputs runs logs
+python - <<'PY'
+import swift, sys
+print("python", sys.version)
+print("swift", getattr(swift, "__version__", "unknown"))
+PY

scripts/swift_train_common.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+
+ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "${ROOT_DIR}"
+
+export http_proxy="${http_proxy:-http://100.72.0.101:8888}"
+export https_proxy="${https_proxy:-http://100.72.0.101:8888}"
+export HTTP_PROXY="${HTTP_PROXY:-${http_proxy}}"
+export HTTPS_PROXY="${HTTPS_PROXY:-${https_proxy}}"
+export HF_ENDPOINT="${HF_ENDPOINT:-https://hf-mirror.com}"
+export TOKENIZERS_PARALLELISM="${TOKENIZERS_PARALLELISM:-false}"
+
+if [[ -f .venv/bin/activate ]]; then
+  source .venv/bin/activate
+elif [[ "${DRY_RUN:-0}" != "1" ]]; then
+  echo "Missing .venv. Run ./scripts/setup_env.sh first." >&2
+  exit 2
+fi
+mkdir -p outputs runs logs
+
+TRAIN_JSONL="${TRAIN_JSONL:-data/processed/training_probe/train.jsonl}"
+VAL_JSONL="${VAL_JSONL:-data/processed/training_probe/validation.jsonl}"
+MAX_LENGTH="${MAX_LENGTH:-262144}"
+SAVE_STEPS="${SAVE_STEPS:-1000}"
+EVAL_STEPS="${EVAL_STEPS:-1000}"
+LOGGING_STEPS="${LOGGING_STEPS:-1}"
+GRAD_ACCUM_STEPS="${GRAD_ACCUM_STEPS:-1}"
+PER_DEVICE_BATCH_SIZE="${PER_DEVICE_BATCH_SIZE:-1}"
+NUM_EPOCHS="${NUM_EPOCHS:-1}"
+LEARNING_RATE="${LEARNING_RATE:-1e-5}"
+WARMUP_RATIO="${WARMUP_RATIO:-0.1}"
+LORA_RANK="${LORA_RANK:-32}"
+
+require_file() {
+  if [[ ! -f "$1" ]]; then
+    echo "Missing required file: $1" >&2
+    exit 2
+  fi
+}
+
+run_swift_train() {
+  local model_path="$1"
+  local train_type="$2"
+  local run_name="$3"
+  local output_dir="outputs/${run_name}"
+  local tb_dir="runs/${run_name}"
+  local log_file="logs/${run_name}.log"
+
+  require_file "${TRAIN_JSONL}"
+  require_file "${VAL_JSONL}"
+  mkdir -p "${output_dir}" "${tb_dir}" logs
+
+  local cmd=(
+    swift sft
+    --model "${model_path}"
+    --dataset "${TRAIN_JSONL}"
+    --val_dataset "${VAL_JSONL}"
+    --train_type "${train_type}"
+    --torch_dtype bfloat16
+    --num_train_epochs "${NUM_EPOCHS}"
+    --per_device_train_batch_size "${PER_DEVICE_BATCH_SIZE}"
+    --per_device_eval_batch_size 1
+    --gradient_accumulation_steps "${GRAD_ACCUM_STEPS}"
+    --learning_rate "${LEARNING_RATE}"
+    --warmup_ratio "${WARMUP_RATIO}"
+    --max_length "${MAX_LENGTH}"
+    --save_steps "${SAVE_STEPS}"
+    --eval_steps "${EVAL_STEPS}"
+    --logging_steps "${LOGGING_STEPS}"
+    --report_to tensorboard
+    --logging_dir "${tb_dir}"
+    --output_dir "${output_dir}"
+    --save_total_limit "${SAVE_TOTAL_LIMIT:-3}"
+    --dataloader_num_workers "${DATALOADER_NUM_WORKERS:-4}"
+  )
+
+  if [[ "${train_type}" == "lora" ]]; then
+    cmd+=(--lora_rank "${LORA_RANK}")
+  fi
+
+  printf '%q ' "${cmd[@]}" | tee "${log_file}.cmd"
+  echo
+  if [[ "${DRY_RUN:-0}" == "1" ]]; then
+    return 0
+  fi
+  "${cmd[@]}" 2>&1 | tee "${log_file}"
+}

scripts/tensorboard.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "${ROOT_DIR}"
+source .venv/bin/activate
+tensorboard --logdir runs --host 0.0.0.0 --port "${TENSORBOARD_PORT:-6006}"

scripts/train_qwen35_9b_full.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+source "$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)/swift_train_common.sh"
+run_swift_train "${QWEN35_9B_MODEL_PATH:-models/qwen3.5-9b}" full qwen35_9b_full_bf16

scripts/train_qwen35_9b_lora.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+source "$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)/swift_train_common.sh"
+run_swift_train "${QWEN35_9B_MODEL_PATH:-models/qwen3.5-9b}" lora qwen35_9b_lora_r32

scripts/train_qwen36_27b_full.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+source "$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)/swift_train_common.sh"
+run_swift_train "${QWEN36_27B_MODEL_PATH:-models/qwen3.6-27b}" full qwen36_27b_full_bf16

scripts/train_qwen36_27b_lora.sh

@@ -0,0 +1,4 @@
+#!/usr/bin/env bash
+set -euo pipefail
+source "$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)/swift_train_common.sh"
+run_swift_train "${QWEN36_27B_MODEL_PATH:-models/qwen3.6-27b}" lora qwen36_27b_lora_r32

scripts/validate_setup.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+
+ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "${ROOT_DIR}"
+
+test -d third_party/modelscope-swift
+python3 -m py_compile scripts/download_dataset.py
+bash -n scripts/*.sh
+
+DRY_RUN=1 TRAIN_JSONL=/tmp/train.jsonl VAL_JSONL=/tmp/validation.jsonl bash -c '
+  echo "{}" > /tmp/train.jsonl
+  echo "{}" > /tmp/validation.jsonl
+  ./scripts/train_qwen35_9b_lora.sh
+  ./scripts/train_qwen35_9b_full.sh
+  ./scripts/train_qwen36_27b_lora.sh
+  ./scripts/train_qwen36_27b_full.sh
+'

src/ti_coding_agent_probe/__init__.py

@@ -0,0 +1,4 @@
+"""Utilities for the TI coding-agent SFT probe."""
+
+__all__ = ["__version__"]
+__version__ = "0.1.0"

src/ti_coding_agent_probe/paths.py

@@ -0,0 +1,16 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+DATA_DIR = REPO_ROOT / "data"
+MODEL_DIR = REPO_ROOT / "models"
+OUTPUT_DIR = REPO_ROOT / "outputs"
+RUNS_DIR = REPO_ROOT / "runs"
+LOG_DIR = REPO_ROOT / "logs"
+
+
+def ensure_project_dirs() -> None:
+    for path in (DATA_DIR, MODEL_DIR, OUTPUT_DIR, RUNS_DIR, LOG_DIR):
+        path.mkdir(parents=True, exist_ok=True)